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学者姓名：陈惠鹏
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Flexible organic synaptic transistors (FOSTs) are crucial for neuromorphic computing due to their flexibility and biocompatibility, yet their mechanical stability under strain is underexplored. This study enhances FOST resilience by optimizing the neutral-axis alignment through layer thickness adjustments and incorporation of a polyimide layer, aligning the axis closer to the heterojunction interface. This strategy significantly reduces strain-induced defects, minimizing excitatory postsynaptic current (EPSC) degradation from 21.19% to 13.34% after 100 bending cycles. Optimized FOSTs demonstrate a remarkable pattern recognition accuracy of 90.4% after bending, significantly outperforming the 76.8% achieved by standard devices. These findings present a straightforward and effective approach to improve the mechanical stability and synaptic performance of FOSTs, advancing the development of durable bio-inspired computing systems.
Keyword ：
Accuracy Accuracy Bending Bending Films Films Flexible synaptic transistor Flexible synaptic transistor mechanical stability mechanical stability Neuromorphics Neuromorphics neutral axis neutral axis pattern recognition pattern recognition Pattern recognition Pattern recognition Performance evaluation Performance evaluation Strain Strain Substrates Substrates Thermal stability Thermal stability Transistors Transistors
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| GB/T 7714 | Ma, Xiao , Zhuang, Bingyong , Chen, Huipeng . Optimizing Neutral-Axis Alignment for Improved Stability and Synaptic Performance in Flexible Transistors [J]. | IEEE ELECTRON DEVICE LETTERS , 2025 , 46 (3) : 444-447 . |
| MLA | Ma, Xiao 等. "Optimizing Neutral-Axis Alignment for Improved Stability and Synaptic Performance in Flexible Transistors" . | IEEE ELECTRON DEVICE LETTERS 46 . 3 (2025) : 444-447 . |
| APA | Ma, Xiao , Zhuang, Bingyong , Chen, Huipeng . Optimizing Neutral-Axis Alignment for Improved Stability and Synaptic Performance in Flexible Transistors . | IEEE ELECTRON DEVICE LETTERS , 2025 , 46 (3) , 444-447 . |
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Memristor-based physical reservoir computing holds significant potential for efficiently processing complex spatiotemporal data, which is crucial for advancing artificial intelligence. However, owing to the single physical node mapping characteristic of traditional memristor reservoir computing, it inevitably induces high repeatability of eigenvalues to a certain extent and significantly limits the efficiency and performance of memristor-based reservoir computing for complex tasks. Hence, this work firstly reports an artificial light-emitting synaptic (LES) device with dual photoelectric output for reservoir computing, and a reservoir system with mixed physical nodes is proposed. The system effectively transforms the input signal into two eigenvalue outputs using a mixed physical node reservoir comprising distinct physical quantities, namely optical output with nonlinear optical effects and electrical output with memory characteristics. Unlike previously reported memristor-based reservoir systems, which pursue rich reservoir states in one physical dimension, our mixed physical node reservoir system can obtain reservoir states in two physical dimensions with one input without increasing the number and types of devices. The recognition rate of the artificial light-emitting synaptic reservoir system can achieve 97.22% in MNIST recognition. Furthermore, the recognition task of multichannel images can be realized through the nonlinear mapping of the photoelectric dual reservoir, resulting in a recognition accuracy of 99.25%. The mixed physical node reservoir computing proposed in this work is promising for implementing the development of photoelectric mixed neural networks and material-algorithm collaborative design. © The Author(s) 2024.
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| GB/T 7714 | Lian, M. , Gao, C. , Lin, Z. et al. Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output [J]. | Light: Science and Applications , 2024 , 13 (1) . |
| MLA | Lian, M. et al. "Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output" . | Light: Science and Applications 13 . 1 (2024) . |
| APA | Lian, M. , Gao, C. , Lin, Z. , Shan, L. , Chen, C. , Zou, Y. et al. Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output . | Light: Science and Applications , 2024 , 13 (1) . |
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As reported, neuromorphic devices have been widely applied in many fields. However, there are few reports on the application of neuromorphic devices in the field of display. Intelligent displays will inevitably be a requirement for high-performance displays in the future. In order to achieve intelligent display, neuromorphic devices must be applied in low-power, high-resolution display fields. Therefore, in this work, we propose a novel global driving strategy (GDS), where the display driving circuit is developed by applying neuromorphic devices (neurons and synapses) to the circuit. It combines the advantages of neurons and synapses, achieving a low-power, high-resolution display while reducing the row circuit area in the driving circuit by half and increasing circuit integration. In addition, based on the strategy, we built a 4×4 array circuit simulation model for display. IEEE
Keyword ：
Circuits Circuits display driving circuit display driving circuit Gray-scale Gray-scale intelligent display intelligent display neuromorphic devices neuromorphic devices Neuromorphics Neuromorphics Neurons Neurons Sputtering Sputtering Synapses Synapses Transistors Transistors
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| GB/T 7714 | Liu, S. , Zhang, X. , Ren, Z. et al. Neuromorphic Global Driving Strategy for Display [J]. | IEEE Electron Device Letters , 2024 , 45 (8) : 1-1 . |
| MLA | Liu, S. et al. "Neuromorphic Global Driving Strategy for Display" . | IEEE Electron Device Letters 45 . 8 (2024) : 1-1 . |
| APA | Liu, S. , Zhang, X. , Ren, Z. , Cai, Y. , Liu, D. , Guo, T. et al. Neuromorphic Global Driving Strategy for Display . | IEEE Electron Device Letters , 2024 , 45 (8) , 1-1 . |
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The large-scale fabrication and patterning of artificial perceptual systems are vital for the development of bionic systems. Traditional patterning processes are often constrained by the use of mask versions, which are not only expensive but also challenging to produce on a large scale. Inkjet printing technology with maskless patterning capability is well suited for current demands for large-scale preparation and patterning. However, the application of printing techniques is typically confined to the production of simple devices or the preparation of patterned active layers within more complex devices. In this study, we successfully fabricated fully inkjet-printed indium gallium zinc oxide (IGZO) memristor arrays to mimic artificial nociceptor (pain receptors). By integrating a layer of silver into the all metal-oxide indium tin oxide (ITO)/IGZO/ITO memristor, we achieved stable threshold switching characteristics, a large current switching ratio of 105, excellent switching durability of 104 cycles scans, and excellent spatial uniformity. We investigated the Ag-based conductive filament conduction mode and mimic LIF characteristics (leaky, integrate and fire), demonstrating the potential of the memristor as an artificial neuron. Lastly, we successfully implemented artificial nociceptor, including “threshold fire”, “relaxation”, “non-adaptation”, and “sensitization”, leveraging the stable threshold switching properties of the device. Our work demonstrates the significant potential of inkjet printing technology in the realization of bionic systems. (Figure presented.) © Science China Press 2024.
Keyword ：
artificial nociceptor artificial nociceptor crossbar array crossbar array fully printed fully printed IGZO memristor IGZO memristor
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| GB/T 7714 | Peng, W. , Liu, C. , Xu, C. et al. Fully printed IGZO memristor arrays with robust threshold switching characteristics for artificial nociceptors; [具有稳定阈值开关特性的全印刷IGZO忆阻器阵列用 于人工伤害感受器] [J]. | Science China Materials , 2024 , 67 (8) : 2661-2670 . |
| MLA | Peng, W. et al. "Fully printed IGZO memristor arrays with robust threshold switching characteristics for artificial nociceptors; [具有稳定阈值开关特性的全印刷IGZO忆阻器阵列用 于人工伤害感受器]" . | Science China Materials 67 . 8 (2024) : 2661-2670 . |
| APA | Peng, W. , Liu, C. , Xu, C. , Qin, C. , Qin, N. , Chen, H. et al. Fully printed IGZO memristor arrays with robust threshold switching characteristics for artificial nociceptors; [具有稳定阈值开关特性的全印刷IGZO忆阻器阵列用 于人工伤害感受器] . | Science China Materials , 2024 , 67 (8) , 2661-2670 . |
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Display circuits play a pivotal role in visual information interaction, but traditional 2T1C display circuits suffer from the separation of storage and computation. Neuromorphic display combines the artificial synapse and light-emitting units into the pixel circuit, empowering the display with smart interaction capability. In this work, we built a novel ambient light adaptive neuromorphic display platform (ALAND) by combining a display driver circuit with the optoelectronic synaptic transistor. The optoelectronic synaptic transistor can perform multiple conduction states under the control of light and electric stimulation, including a 4-bit optical modulation grayscale. Moreover, it can make adaptive decisions according to various ambient lights. This work provides a potentially valuable novel strategy for a more comfortable and clearer visual information interaction. © 1963-2012 IEEE.
Keyword ：
Ambient light adaptation Ambient light adaptation neuromorphic display neuromorphic display optoelectronic synaptic transistor optoelectronic synaptic transistor
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| GB/T 7714 | Ren, Z. , Zhang, X. , Liu, S. et al. A Novel Neuromorphic Display Platform for Ambient Light Adaptive Display [J]. | IEEE Transactions on Electron Devices , 2024 , 71 (8) : 5146-5149 . |
| MLA | Ren, Z. et al. "A Novel Neuromorphic Display Platform for Ambient Light Adaptive Display" . | IEEE Transactions on Electron Devices 71 . 8 (2024) : 5146-5149 . |
| APA | Ren, Z. , Zhang, X. , Liu, S. , Liu, D. , Qin, N. , Guo, T. et al. A Novel Neuromorphic Display Platform for Ambient Light Adaptive Display . | IEEE Transactions on Electron Devices , 2024 , 71 (8) , 5146-5149 . |
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Neuromorphic visual systems can emulate biological retinal systems to perceive visual information under different levels of illumination, making them have considerable potential for future intelligent vehicles and vision automation. However, the complex circuits and high operating voltages of conventional artificial vision systems present great challenges for device integration and power consumption. Here, bioinspired synaptic transistors based on organic single crystal phototransistors are reported, which exhibit excitation and inhibition synaptic plasticity with time-varying. By manipulating the charge dynamics of the trapping centers of organic crystal-electret vertical stacks, organic transistors can operate below 1 V with record high on/off ratios close to 108 and sharp switching with a subthreshold swing of 59.8 mV dec-1. Moreover, the approach offers visual adaptation with highly localized modulation and over 98.2% recognition accuracy under different illumination levels. These bioinspired visual adaptation transistors offer great potential for simplifying the circuitry of artificial vision systems and will contribute to the development of machine vision applications. A bioinspired synaptic transistor based on organic crystal-electret stacks is developed, which presents visual adaption with highly localized modulation and over 98.2% recognition accuracy. By manipulating the charge dynamics of the trapping centers, organic transistors can operate below 1 V with record high on/off ratio close to 108 and sharp switching of a subthreshold swing of 59.8 mV dec-1. image
Keyword ：
organic field-effect transistor organic field-effect transistor steep switching steep switching ultralow voltage ultralow voltage visual adaption visual adaption
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| GB/T 7714 | Duan, Shuming , Zhang, Xianghong , Xi, Yue et al. Solution-Processed Ultralow Voltage Organic Transistors With Sharp Switching for Adaptive Visual Perception [J]. | ADVANCED MATERIALS , 2024 , 36 (32) . |
| MLA | Duan, Shuming et al. "Solution-Processed Ultralow Voltage Organic Transistors With Sharp Switching for Adaptive Visual Perception" . | ADVANCED MATERIALS 36 . 32 (2024) . |
| APA | Duan, Shuming , Zhang, Xianghong , Xi, Yue , Liu, Di , Zhang, Xiaotao , Li, Chunlei et al. Solution-Processed Ultralow Voltage Organic Transistors With Sharp Switching for Adaptive Visual Perception . | ADVANCED MATERIALS , 2024 , 36 (32) . |
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Artificial neuromorphic tactile systems based on tactile sensors and artificial neurons can simulate biological perception systems and achieve the efficient transmission, processing and utilization of information. However, limited by the materials and structures of devices, it is difficult for artificial neuromorphic tactile systems to have stable perception abilities such as biological functions, especially when the devices are bending or stretching. In this work, we propose a performance-stable artificial tactile neuron, which integrates a stretch-insensitive triboelectric nanogenerator (TENG) with an artificial neuron in a single device. The stable contact triboelectric output could stimulate a neuron and affect its firing time. Based on this principle, we established a 64 x 64 neuromorphic tactile matrix, which processed the triboelectric signal generated by touch through the integrate-and-fire neuron and simulated the pressure trajectory and texture of tactile perception based on the firing time classification. This provides an effective strategy for the future simulation of biological skin perception and fast classification and recognition of information. A performance-stable tactile neuron is developed, which integrates a stretch-insensitive triboelectric nanogenerator with an artificial neuron in a single device, and a 64 x 64 neuromorphic tactile matrix is established to process touch signals.
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| GB/T 7714 | Liu, Yaqian , Wang, Hui , Lin, Jiaming et al. Stretchable and stable neuromorphic tactile system [J]. | JOURNAL OF MATERIALS CHEMISTRY C , 2024 , 12 (29) : 10979-10984 . |
| MLA | Liu, Yaqian et al. "Stretchable and stable neuromorphic tactile system" . | JOURNAL OF MATERIALS CHEMISTRY C 12 . 29 (2024) : 10979-10984 . |
| APA | Liu, Yaqian , Wang, Hui , Lin, Jiaming , Ye, Weixi , Rao, Zhichao , Lu, Wenjie et al. Stretchable and stable neuromorphic tactile system . | JOURNAL OF MATERIALS CHEMISTRY C , 2024 , 12 (29) , 10979-10984 . |
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Memristor-based physical reservoir computing holds significant potential for efficiently processing complex spatiotemporal data, which is crucial for advancing artificial intelligence. However, owing to the single physical node mapping characteristic of traditional memristor reservoir computing, it inevitably induces high repeatability of eigenvalues to a certain extent and significantly limits the efficiency and performance of memristor-based reservoir computing for complex tasks. Hence, this work firstly reports an artificial light-emitting synaptic (LES) device with dual photoelectric output for reservoir computing, and a reservoir system with mixed physical nodes is proposed. The system effectively transforms the input signal into two eigenvalue outputs using a mixed physical node reservoir comprising distinct physical quantities, namely optical output with nonlinear optical effects and electrical output with memory characteristics. Unlike previously reported memristor-based reservoir systems, which pursue rich reservoir states in one physical dimension, our mixed physical node reservoir system can obtain reservoir states in two physical dimensions with one input without increasing the number and types of devices. The recognition rate of the artificial light-emitting synaptic reservoir system can achieve 97.22% in MNIST recognition. Furthermore, the recognition task of multichannel images can be realized through the nonlinear mapping of the photoelectric dual reservoir, resulting in a recognition accuracy of 99.25%. The mixed physical node reservoir computing proposed in this work is promising for implementing the development of photoelectric mixed neural networks and material-algorithm collaborative design. This manuscript proposes a photoelectric dual-output mixed physical node reservoir system. It achieves higher handwriting digit recognition accuracy and use the photoelectric output characteristics to achieve multichannel image recognition.
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| GB/T 7714 | Lian, Minrui , Gao, Changsong , Lin, Zhenyuan et al. Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output [J]. | LIGHT-SCIENCE & APPLICATIONS , 2024 , 13 (1) . |
| MLA | Lian, Minrui et al. "Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output" . | LIGHT-SCIENCE & APPLICATIONS 13 . 1 (2024) . |
| APA | Lian, Minrui , Gao, Changsong , Lin, Zhenyuan , Shan, Liuting , Chen, Cong , Zou, Yi et al. Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output . | LIGHT-SCIENCE & APPLICATIONS , 2024 , 13 (1) . |
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大学物理作为理工科的课程,其教学内容不仅涉及自然科学知识,更蕴含着丰富的思政教育资源.该门课程具备的知识广度、深度和时间跨度,使其与描述世界普遍规律的马克思主义哲学原理天然契合.通过在大学物理课程中实施唯物论、辩证法、认识论和唯物史观的四维融入,不仅可以培养学生形成正确的世界观、人生观和价值观,还可以培养践行社会主义核心价值观.对此,本文通过分析大学物理课程与马克思主义哲学原理天然契合的依据,在此基础上研究大学物理课程融入马克思主义哲学原理的教学实践及教学方法,以期促进学生提升物理学习水平的同时,提升思想政治水平.
Keyword ：
大学物理 大学物理 实施研究 实施研究 课程思政 课程思政
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| GB/T 7714 | 陈惠鹏 , 陈绍敏 . 大学物理教学中实施课程思政的研究 [J]. | 国家通用语言文字教学与研究 , 2024 , (11) : 1-3 . |
| MLA | 陈惠鹏 et al. "大学物理教学中实施课程思政的研究" . | 国家通用语言文字教学与研究 11 (2024) : 1-3 . |
| APA | 陈惠鹏 , 陈绍敏 . 大学物理教学中实施课程思政的研究 . | 国家通用语言文字教学与研究 , 2024 , (11) , 1-3 . |
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Machine vision enables machines to extract rich information from image or video data and make intelligent decisions. However, approaches using artificial synapse hardware systems significantly limit the real-time and accuracy in machine vision segmentation amid complex environments. Addressing this, we propose a novel three-terminal adaptive artificial-light-emitting synapse (AALS) capable of photoelectric double output along with adaptive behavior. The device uses silver nanowires (AgNWs) as polar conductive bridges to reduce reliance on transparent electrodes, while polyvinyl alcohol (PVA) dielectric layers adaptively modulate charge carrier concentrations in conductive channels. Additionally, we have designed an adaptive parallel neural network (APNN) and applied it to autonomous driving image processing. This innovation significantly reduces adaptation time and notably enhances mean pixel accuracy (MPA) for semantic segmentation under overexposure and low-light conditions by 142.2% and 304.4%, respectively. Therefore, this work introduces new strategies for advanced adaptive vision, promising significant potential in intelligent driving and neuromorphic computing.
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| GB/T 7714 | Chen, Cong , Chen, Zhenjia , Liu, Di et al. Three-terminal quantum dot light-emitting synapse with active adaptive photoelectric outputs for complex image processing/parallel computing [J]. | MATTER , 2024 , 7 (11) . |
| MLA | Chen, Cong et al. "Three-terminal quantum dot light-emitting synapse with active adaptive photoelectric outputs for complex image processing/parallel computing" . | MATTER 7 . 11 (2024) . |
| APA | Chen, Cong , Chen, Zhenjia , Liu, Di , Zhang, Xianghong , Gao, Changsong , Shan, Liuting et al. Three-terminal quantum dot light-emitting synapse with active adaptive photoelectric outputs for complex image processing/parallel computing . | MATTER , 2024 , 7 (11) . |
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